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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach [Mīkstie vāki]

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(Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia)
  • Formāts: Paperback / softback, 456 pages, height x width: 276x216 mm, weight: 1220 g, 101 illustrations (1 in full color); Illustrations, unspecified
  • Izdošanas datums: 19-Mar-2019
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0128174447
  • ISBN-13: 9780128174449
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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based ApproachPalielināt
  • Formāts: Paperback / softback, 456 pages, height x width: 276x216 mm, weight: 1220 g, 101 illustrations (1 in full color); Illustrations, unspecified
  • Izdošanas datums: 19-Mar-2019
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0128174447
  • ISBN-13: 9780128174449
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780128174449.html
Keywords:

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more.

This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.

  • Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction
  • Explains how to apply machine learning techniques to EEG, ECG and EMG signals
  • Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series
Preface ix
Acknowledgments xi
1 Introduction and Background
1.1 Electroencephalography
1(4)
1.2 Electromyography
5(5)
1.3 Electrocardiography
10(3)
1.4 Phonocardiography
13(3)
1.5 Photoplethysmography
16(1)
1.6 Other Biomedical Signals
17(1)
1.6.1 The Electroneurogram
17(1)
1.6.2 The Electroretinogram
17(1)
1.6.3 The Electrooculogram
18(1)
1.6.4 The Eleclrogastrogram
18(1)
1.6.5 The Carotid Pulse
18(1)
1.6.6 The Vibromyogram
18(1)
1.7 Machine Learning Methods
18(1)
References
19(8)
2 Biomedical Signals
2.1 The Electroencephalogram
27(21)
2.1.1 Introduction
27(1)
2.1.2 The Nervous System
27(1)
2.1.3 The Brain
27(1)
2.1.4 Electroencephalography
27(2)
2.1.5 Historical Perspective
29(1)
2.1.6 EEG Recording Techniques
29(2)
2.1.7 The EEG Measured on the Scalp
31(1)
2.1.8 EEG Rhythms and Waveforms
31(1)
2.1.9 Uses of EEG Signals in Epileptic Seizure Detection and Prediction
32(5)
2.1.10 Uses of EEG Signals in Brain-Computer Interfacing
37(3)
2.1.11 Uses of EEG Signals in Migraine Detection
40(1)
2.1.12 Uses of EEG Signals in Source Localization
41(2)
2.1.13 Uses of EEG Signals in Sleep
43(3)
2.1.14 Uses of EEG Signal for Emotion Recognition
46(2)
2.1.15 Freiburg LEG Database for Epileptic Seizure Prediction and Detection
48(1)
2.2 The Electromyogram
48(14)
2.2.1 Introduction
48(1)
2.2.2 The Electromyograph and Instrumentation
49(1)
2.2.3 EMG Electrodes
49(1)
2.2.4 Signal Acquisition
50(1)
2.2.5 Signal Amplification and Tillering
50(1)
2.2.6 Signal Digitization
50(1)
2.2.7 The Motor Unit Action Potential
51(2)
2.2.8 Myoelectric Signal Recording
53(1)
2.2.9 Neuromuscular Disorders
54(1)
2.2.10 Uses of EMG Signals in Diagnosis of Neuromuscular Disorders
55(1)
2.2.11 Uses of EMG Signals in Prosthesis Control
56(3)
2.2.12 Uses of EMG Signals in Rehabilitation Robotics
59(3)
2.2.13 Other EMG Applications
62(1)
2.3 The Electrocardiogram
62(12)
2.3.1 Introduction
62(1)
2.3.2 Electrocardiogram Signals
63(1)
2.3.3 Physiology
63(1)
2.3.4 The EGG Waveform
64(1)
2.3.5 Heart Diseases
65(1)
2.3.6 Uses of ECG Signals in Diagnosis of Heart Arrhythmia
65(4)
2.3.7 Uses of ECG Signals in Congestive Heart Failure Detection
69(2)
2.3.8 Uses of ECG Signals in Sleep Apnea Detection
71(1)
2.3.9 Uses of ECG Signals in Fetal Analysis
72(2)
2.4 Phonocardiogram
74(5)
2.4.1 Heart Murmurs
74(1)
2.4.2 First Heart Sound (S1)
74(1)
2.4.3 Second Heart Sound (S2)
75(1)
2.4.4 Third Heart Sound (S3)
76(1)
2.4.5 Fourth Heart Sound (S4)
76(1)
2.4.6 Uses of PCG Signals in Diagnosis of Heart Diseases
76(3)
2.5 Photoplethysmography
79(2)
2.6 Other Biomedical Signals
81(1)
2.6.1 Electroneurogram
1(80)
2.0.2 Electroretinogram
81(1)
2.6.3 Electrooculogram
81(1)
2.6.4 Electrogastrogram
81(1)
2.6.5 Carotid Pulse
82(1)
2.6.6 Vibromyogram
82(1)
2.6.7 Vibroarthrogram
82(1)
References
82(5)
Further Reading
87(2)
3 Biomedical Signal Processing Techniques
3.1 Introduction to Spectral Analysis
89(1)
3.2 Power Spectral Density
89(16)
3.2.1 Continuous-Time Fourier Series Analysis
89(1)
3.2.2 Discrete-Time Fourier Series Analysis
90(6)
3.2.3 Frequency Resolution
96(2)
3.2.4 Windowing Techniques
98(1)
3.2.5 Periodogram Power Spectral Density
98(6)
3.2.6 Welch Power Spectral Density
104(1)
3.3 Parametric Model-Based Methods
105(25)
3.3.1 Autoregressive Model for Spectral Analysis
111(1)
3.3.2 Yule-Walker AR Modeling
112(7)
3.3.3 Covariance Method
119(7)
3.3.4 Modified Covariance Method
126(1)
3.3.5 Burg Method
127(3)
3.4 Subspace-Based Methods for Spectral Analysis
130(4)
3.4.1 MUSIC Modeling
130(2)
3.4.2 Eigenvector Modeling
132(2)
3.5 Time-Frequency Analysis
134(56)
3.5.1 Short-Time Fourier Transform: The Spectrogram
135(2)
3.5.2 Wigner-Ville Distribution
137(3)
3.5.3 Choi-Williams Distribution
140(2)
3.5.4 Analytic Signal
142(1)
3.5.5 Wavelet Analysis
142(1)
3.5.6 Continuous Wavelet Transform
143(2)
3.5.7 Discrete Wavelet Transform
145(3)
3.5.8 Stationary Wavelet Transform
148(5)
3.5.9 Wavelet Packet Decomposition
153(2)
3.5.10 Dual Tree Complex Wavelet Transform
155(7)
3.5.11 Tunable Q-Factor Wavelet Transform
162(2)
3.5.12 Flexible Analytic Wavelet Transform
164(5)
3.5.13 Empirical Wavelet Transform
169(5)
3.5.14 Empirical Mode Decomposition
174(6)
3.5.15 Ensemble Empirical Mode Decomposition
180(3)
3.5.16 Complete Ensemble Empirical Mode Decomposition
183(7)
References
190(3)
4 Feature Extraction and Dimension Reduction
4.1 Introduction
193(1)
4.2 Feature Extraction Methods
194(5)
4.2.1 Examples for Feature Extraction
194(5)
4.3 Dimension Reduction/Feature Selection Methods
199(72)
4.3.1 Statistical Features
199(1)
4.3.2 Examples With Statistical Features
200(60)
4.3.3 Entropy
260(1)
4.3.4 Kolmogorov Entropy
260(1)
4.3.5 Approximate and Sample Entropy
260(1)
4.3.6 Detrended Fluctuation Analysis
261(6)
4.3.7 Principal Component Analysis
267(2)
4.3.8 Independent Component Analysis
269(1)
4.3.9 Linear Discriminant Analysis
270(1)
4.4 Electrocardiogram Signal Preprocessing
271(4)
4.4.1 QRS Detection Algorithms
272(3)
References
275(2)
5 Biomedical Signal Classification Methods
5.1 Introduction
277(1)
5.2 Performance Evaluation Metrics
277(4)
5.3 Linear Discriminant Analysis
281(19)
5.4 Naive Bayes
300(14)
5.5 k-Nearest Neighbor
314(12)
5.6 Artificial Neural Networks
326(48)
5.7 Support Vector Machines
374(16)
5.8 Decision Tree (DT)
390(20)
5.9 Deep Learning
410(24)
References
434(1)
Index 435
Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland